TLDRai.com Too Long; Didn't Read AI TLDWai.com Too Long; Didn't Watch AI
Gba lati ayelujara
Ṣe awọn akojọpọ ailopin pẹlu AI!
Igbesoke si PRO US$ 7.0/m
Ko si awọn iṣẹ ihamọ

Introduction to Machine Learning: Module 6.9 GMM-EM hyper-parameter tuning

https://www.youtube.com/watch?v=oTShMHt3psA&t=2s
The speaker is discussing the use of Bayesian Information Criterion (BIC) in Gaussian Mixture Model Expectation-Maximization (GMM-EM) clustering. The BIC measures how well a model fits the data, and it helps determine the number of clusters in a dataset. GMM-EM can capture differences in probability among clusters, which can impact the choice of the number of clusters. In contrast, K-means assumes equally likely clusters, while GMM-EM can encode probabilities of belonging to different clusters. The lecture highlights similarities and differences between these two clustering algorithms.
Awọn olumulo PRO gba awọn akopọ Didara Giga julọ
Igbesoke si PRO US$ 7.0/m
Ko si awọn iṣẹ ihamọ
Ṣe akopọ fidio agbegbe Ṣe akopọ fidio ori ayelujara

Gba awọn abajade didara to dara julọ pẹlu awọn ẹya diẹ sii

Di PRO
Machine Learning Module GMM-EM hyper-parameter hyper-parameter tuning Learning Machine Introduction GMM-EM tuning hyper-parameter

Awọn akopọ ti o jọmọ

Gba lati ayelujara
Ṣe akopọ fidio agbegbe Ṣe akopọ fidio ori ayelujara

Gba awọn abajade didara to dara julọ pẹlu awọn ẹya diẹ sii

Di PRO




Lapapọ akojọpọ awọn fidio ti a ṣe lori TLDWai.com:

2,635

Ilana asiri Awọn ofin ti iṣẹ Pe wa Developers

A nireti pe o gbadun ohun elo AI wa. Ise agbese wa ni idagbasoke pẹlu Django .
© 2024 TLDWai.com| VPS.org LLC | Ṣe nipasẹ Lou